Merge branch 'main' into 99_review_vignette_transformator@main

DESCRIPTION

@@ -1,8 +1,8 @@
 Type: Package
 Package: teal
 Title: Exploratory Web Apps for Analyzing Clinical Trials Data
-Version: 0.15.2.9117
-Date: 2025-01-30
+Version: 0.15.2.9118
+Date: 2025-01-31
 Authors@R: c(
     person("Dawid", "Kaledkowski", , "[email protected]", role = c("aut", "cre"),
            comment = c(ORCID = "0000-0001-9533-457X")),

NEWS.md

@@ -1,4 +1,4 @@
-# teal 0.15.2.9117
+# teal 0.15.2.9118
 
 ### New features
 

vignettes/adding-support-for-reporting.Rmd

@@ -29,8 +29,9 @@ The entire life cycle of objects involved in creating the report and configuring
 ## Custom module
 
 Let us consider an example module, based on the example module from `teal`:
-```{r, message=FALSE}
+```{r message=FALSE, warning=FALSE}
 library(teal)
+
 my_module <- function(label = "example teal module") {
   module(
     label = label,
@@ -255,7 +256,7 @@ In the final example, we have incorporated `teal.code` snippets.
 This allows `ReporterCard` to store the code necessary to generate the table along with the table itself.
 To learn more about `teal.code` see the vignette _`qenv`_ in  `teal.code`.
 
-```{r}
+```{r message=FALSE, warning=FALSE}
 library(teal)
 library(teal.reporter)
 

vignettes/data-as-shiny-module.Rmd

@@ -31,12 +31,9 @@ One case for postponing data operations is datasets that are dynamic, frequently
 Such data cannot be created once and kept in the global environment.
 Using `teal_data_module` enables creating a dataset from scratch every time the user starts the application.
 
-```{r, message = FALSE, warning = FALSE}
+```{r message = FALSE, warning = FALSE}
 library(teal)
-```
-
 
-```{r}
 data_module <- teal_data_module(
   ui = function(id) tags$div(),
   server = function(id) {

vignettes/data-transform-as-shiny-module.Rmd

@@ -27,11 +27,9 @@ This vignette presents the way on how to manage custom data transformations in `
 
 Let's initialize a simple `teal` app by providing `iris` and `mtcars` as input datasets.
 
-```{r, message = FALSE, warning = FALSE}
+```{r message = FALSE, warning = FALSE}
 library(teal)
-```
 
-```{r}
 data <- within(teal_data(), {
   iris <- iris
   mtcars <- mtcars

vignettes/decorate-module-output.Rmd

@@ -58,7 +58,7 @@ This approach adds functionality solely to the server code of the module.
 In the following example, we assume that the module contains an object (of class `ggplot2`) named `plot`. 
 We modify the title and x-axis label of plot:
 
-```{r}
+```{r message=FALSE, warning=FALSE}
 library(teal)
 static_decorator <- teal_transform_module(
   label = "Static decorator",

vignettes/getting-started-with-teal.Rmd

@@ -29,7 +29,7 @@ More advanced users of the framework can also create new analysis modules which
 
 This simple `teal` application takes the `iris` and `mtcars` datasets and displays their contents:
 
-```{r, message=FALSE}
+```{r message=FALSE, warning=FALSE}
 library(teal)
 
 app <- init(

vignettes/including-data-in-teal-applications.Rmd

@@ -36,7 +36,7 @@ Thus, the first step of building a `teal` app is creating a `teal_data` object.
 
 A `teal_data` object is created by calling the `teal_data` function and passing data objects as `name:value` pairs.
 
-```{r, message=FALSE}
+```{r message=FALSE, warning=FALSE}
 library(teal)
 
 # create teal_data